{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">微信搜索 **[【K同学啊】](https://mp.weixin.qq.com/s/NES9RhtAhbX_jsmGua28dA)**  关注这个分享干货的博主。\n",
    "\n",
    "\n",
    ">📍 本文 **GitHub** [https://github.com/kzbkzb/Python-AI](https://github.com/kzbkzb/Python-AI) 已收录"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、前期工作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我的环境：\n",
    "\n",
    "- 语言环境：Python3.6.5\n",
    "- 编译器：jupyter notebook\n",
    "- 深度学习环境：TensorFlow2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "来自专栏：[**【深度学习100例】**](https://blog.csdn.net/qq_38251616/category_11068756.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 设置GPU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果使用的是CPU可以忽略这步"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "gpus = tf.config.list_physical_devices(\"GPU\")\n",
    "\n",
    "if gpus:\n",
    "    gpu0 = gpus[0]                                        #如果有多个GPU，仅使用第0个GPU\n",
    "    tf.config.experimental.set_memory_growth(gpu0, True)  #设置GPU显存用量按需使用\n",
    "    tf.config.set_visible_devices([gpu0],\"GPU\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = datasets.fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28), (10000, 28, 28), (60000,), (10000,))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将像素的值标准化至0到1的区间内。\n",
    "train_images, test_images = train_images / 255.0, test_images / 255.0\n",
    "\n",
    "train_images.shape,test_images.shape,train_labels.shape,test_labels.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "加载数据集会返回四个 NumPy 数组：\n",
    "\n",
    "- train_images 和 train_labels 数组是训练集，模型用于学习的数据。\n",
    "- test_images 和 test_labels 数组是测试集,会被用来对模型进行测试。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "图像是 28x28 的 NumPy 数组，像素值介于 0 到 255 之间。标签是整数数组，介于 0 到 9 之间。这些标签对应于图像所代表的服装类："
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|标签|类|标签|类|\n",
    "|:--:|:--:|:--:|:--:|\n",
    "|0|T恤/上衣|5|凉鞋\n",
    "|1|\t裤子   |6|衬衫\n",
    "|2|\t套头衫 |7|运动鞋\n",
    "|3|\t连衣裙 |8|包\n",
    "|4|\t外套   |9|短靴"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.调整图片格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((60000, 28, 28, 1), (10000, 28, 28, 1), (60000,), (10000,))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#调整数据到我们需要的格式\n",
    "train_images = train_images.reshape((60000, 28, 28, 1))\n",
    "test_images = test_images.reshape((10000, 28, 28, 1))\n",
    "\n",
    "train_images.shape,test_images.shape,train_labels.shape,test_labels.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABG0AAADqCAYAAAAYlLi0AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAB8XUlEQVR4nO2dd7xcVdX+nw02aoAkkE4KVZKQkNADhCogSO9VfwoWFMSXIgKvIgqICKICCi8iTXo0IBBKKIHQkpBKSIBUSG9URcTz+2Nmdp69Mnsz92buvWdmnu/nk0/WzN5z5szZ9Zy7nrVclmUQQgghhBBCCCGEEPlijbY+ASGEEEIIIYQQQgixKnpoI4QQQgghhBBCCJFD9NBGCCGEEEIIIYQQIofooY0QQgghhBBCCCFEDtFDGyGEEEIIIYQQQogcooc2QgghhBBCCCGEEDnkc02p3KFDh6xnz54tdCqiHLNmzcKSJUtctY6Xlzb817/+5e05c+Z4e8MNNwzqrb322t52zpW17fGWL1/u7S9+8YtBvU6dOnl7zTXXbOppN5uxY8cuybKsY7WO11bt+J///Cd4vWTJEm+3b9/e25///OdX+7s++ugjb3P7AmE/sX2hpajlsfjxxx97+4MPPgjKVqxY4W0eE9yeQDgWY+MNAN5//31vr7HGyr8LbLTRRkG9jh2rNhyaRL2Mxdbkk08+8XY1xvbqUstjkbHz6XvvvedtnlvtWvWlL33J2zzG7PE+/PBDb6+zzjre7tq1a1CPj9GaaCzWPvUyFlPwmmb3lF/4whcqOgavwby3sXvetkJjsfZphLHYCKTGYpMe2vTs2RNjxoypzlmJihg8eHBVj1eNNsyyzNvNvWGeOnWqt8844wxvH3300UG9gQMHepsXx899Luy6U6ZM8fawYcO83bt376Deueee6+0NNtigiWfdfJxzs6t5vLYai4sWLQpe33LLLd4++eSTvc0Px5rL+PHjvf36668HZUcccYS3W+smMo9jsVJmzpzp7WeeeSYo+/vf/+5tfrBy0kknBfW22247b3N73H///UG9J554wtt8o3jiiScG9U477bSKzr3a1MtYbE3mzZvn7S5durThmRTIw1jkdRBo3lpo59ORI0d6+8Ybb/S2Xau23nprb/NNpH2A+sILL3h7p5128vYvf/nLoN5aa61V0flWY+1nNBZrnzyMRYsdmyWa22d5zezTp09Q1q1bt4qOwWsw/76jjjqqWedUbTQWa588jkXRdFJjsUkPbUTjkNqcxRa+V199NXh99913e9ve2PFfDvkv/xdccEFQb9myZRWe8Uq22GILb0+YMCEou+yyy7zNDxa+8pWvBPV+9KMfebtfv35NPod6gttn+PDhQdmtt97q7bvuusvb1ouCH7jxQxbr9cF/jZo7d663Dz300KAe95+8bHramkceecTbV199dVDGN2X//ve/gzL+q/2sWbO8feyxxwb1Fi5c6G3+64t9gNq5c2dvt2vXztv33XdfUO+aa67x9j777OPta6+9FqI8e+21l7ftDXqHDh28zTf8lf6ljB/MAMCee+7p7X/+85/e7tGjR1BvxIgR3uaHdPVIpQ8t2Evmt7/9bVDGDzWtByFfPx6nr7zySlDvgQceKPu99gE2e9S89NJL3t5ll12Cevywdo899vD297///aBeXrwChEjB4zTlRfb22297++abbw7KrrrqKm+zB1w14HOyfxy54oorvH3mmWdWdLz//ve/0eMLIeoHjWwhhBBCCCGEEEKIHKKHNkIIIYQQQgghhBA5RA9thBBCCCGEEEIIIXKIYtqIsqT0+qzv5eCzNn4M64rXXXfdoIxjbLBO3mbJ4GwY7777rrc5k439XOrcd9hhB29zPIHRo0cH9Z5++mlvDxkyJCi7/fbbo8evR7jtOEYJAFx++eXe/sUvfuFtGziY46Fw3BobYHO99dbzNsc5OfDAA4N6NhZOo/LWW295+8477/S2jcPEMUlS+vfu3bt7e/31149+L48xO2b5cxxjw8a+2Xnnnb3NsQU4nhQQxhZodLjtOG4KALzzzjve5va3c++RRx7pbZ7LPv3006AexzriccqZVID6j2NTKTwWDzroIG/boOx8LW0MGh5LHGDYBpjk+S/2GSCMi7N48WJv2yxTPCc//vjj3n7++eeDeqeffrq3Dz/8cAiRByqN6cKJLQDgjTfe8DaPASDcY/IYTmWy5LE9f/78oB6vwbz/tcf7n//5H29zwPC99947qMfrvf29fD0U36Y8Nlh17Jql7idiAa8/63Mx7H0Ixx6bNm2atzluZ3O/qx6pdgDySuEkG2effXZQxgk8eI6xa3WlaDQLIYQQQgghhBBC5BA9tBFCCCGEEEIIIYTIIXUlj7KuUTGXKOve/dxzz3n7gAMOqOj47Epu3f4rpdquda3FYYcd5u05c+Z4e5NNNgnq8W+wrvdWUhGrx9eoffv20Xqxz6RgF1WWAgDhuY8aNSoomzp1qre33nrrir6rXrAuxOwO/L3vfc/bv/vd74J67AqYkkcNGjTI21//+te9zamogVVTijcqLB1KXRN2/bXu2DwWeS7r1atXUI+lcXwMO1fZPlLu2ADwySefeJvTUk+ePDmo99BDD3mbZSeNCKdmnjlzZlDG8+OyZcu8vWDBgqAej02WtE6cODGox27/3Fb8PY1Gal3+8Y9/7G1Oe2/TZLM0yR6PxwivY1YOyvMp2yyHAoAPP/zQ2ympIq9/PFfY4/3hD3/w9n777ReUWRmeEC1JpWm9WYZr1xbes9q+zmOTy+zY4fmVJVG8vwSAL3zhC97m9dPuPfk1zxV//etfg3offfSRt//2t78FZXw9+Drl+b4iT1R6nZpzPTn0AgBMmjTJ2yzXA4ALLrjA29yOjz32WFCvuVKbPFFpP03V49dcr9JnA7zPAcI1k9uJJeYAMH36dG/btZrHZjXGnzxthBBCCCGEEEIIIXKIHtoIIYQQQgghhBBC5JC6kkfZCPLs9v/mm296+6abbgrqsRsjZ8KwbouceSgliWJXLHtOXJY6Rkr+09qMHTs2eM2SqA4dOnjbZqRgOHI+EGY6SWW24WvE1yTlDsuurDY7B2cn6tatW9nvsdjv4v7TaJlt+PoBYQabTTfd1Nv2unB7cxYTlsUAYX/iY9u+VakErt459dRTvX311Vd720ql2A3cykPtGCnB7txA2G6MzTJlM7vF4OOvWLHC2zwuAUmimD59+nj7xRdfDMpSWYRi8PizMtAuXbp4m+dodstvZGx2GJZJ8JiwLte81thryXImXu+snJhf8/pkpY98fK6XylrFMie7B+LzGz58eFB2/PHHQ4jWIiU1GDZsmLd5nuTsiEC437TjlI8fs4FwrFe690+NWT4+j9MePXoE9UaMGOHtRx55JCjjMA+NJomqVApj34+FbLDceuut3t5pp528bdfPa6+91tu8ltosu5wJijMNAcA111zj7QEDBlR0frVKTNqUqpe6T+bxZ+8f+J6f69l18dlnn/U2hwWxe+OtttrK2ywhtsT22k1BnjZCCCGEEEIIIYQQOUQPbYQQQgghhBBCCCFyiB7aCCGEEEIIIYQQQuSQuoppk0orPXLkSG8//vjjQT3WuXK6Wqs35zRr3/rWt7ydSnWd0klyajAbN6XSuBCtwVNPPRW85mvEelz7G1graGMs/OpXv/I2p0e1muN58+aVrWf1wqwV5Jg2Nv3auHHjvM2aUxsDhPXN9nfdf//93m60mDap/rx06dJoGceq6dSpk7ftGOPYN/xdqdR+jQzH2eLUpn//+9+DejvuuKO3rb6X24BTSlvdLo8RjnVh25DHDqcJX7RoUeRXhDFTLr/88mi9Rmfrrbf2tp0DeUxwbDbbjja1dwkbv4R15dymNoZRo7J8+fLgNce04bmL10sgHC92PuW1K5a2Fwjbnts9FVcuFTOA4+xw7Cqet+35PfHEE0GZYtqIliYV54k5/PDDvc192MZz22CDDbxt403wnJcaY1yWirXIpOrFxr1NSc7nfuCBBwZlHG+L91v23FOxHBuZqVOnetteM07ZPWbMGG8vW7YsqHfKKad4e4899vC2jVvDx2AbCNdujsu62WabJc+/1ql0f5+aA7gsFUuGx9vcuXODMh5XHM/TPmvg+8CuXbsGZZWmMq8UedoIIYQQQgghhBBC5BA9tBFCCCGEEEIIIYTIIXXlG2fdwJlXXnnF27NmzQrK2NWY7f322y+o9+qrr3r73HPP9fbgwYODev369fM2u7MDwMsvv1z2nHbZZZegXknqkIfU3/fdd1/wmt3O+HpZV0t2A2eZBBDKy1h2ZtOLf+Mb3/D2H//4R29vs802QT2WafE123jjjYN6P/zhD7193XXXedume+TjsdQAAF5//XVvT58+3ducuq9eSaVT5H5h+y2ndG7Od1m3wpQMoFH5wQ9+4G1OFQmE6ditFJD7N8syUzIYvv72eFyWktW8++673uYUpZLfxOF06Ha+5THHrvQsKwWAgQMHepuvtU21buVXJexc3qhYmRn3e5ZK2evIr60kjVPDcnp3Ts0OhOOU05fatYrdwlmmNWnSpKDegw8+WPZ4dt5muTGn/xaiNYjJIQ455JDgNUuHOIW93ftzPStZikkqqr0nt98b21PZeYTnADuPsITn2GOPLXu8eqVSCYqVdY8ePdrbLCmz6x3fk1x99dXetrKYs88+29ssDbfnx+miOXwDEIby4Daud3mU7euVyg4XLlzobZar2dANfJ/Jn7H3FRwugPsE712BVZ8BtCTytBFCCCGEEEIIIYTIIXpoI4QQQgghhBBCCJFDal4elZJQsGsZR+W27vfs5styF7YBYPvtt/c2u6fZDEXsZvfAAw8EZezSzplfbrzxxqBeSeplXfjaggkTJgSvOcMTu4raLBmMdSdjvvKVr3ibXVmBMIr7r3/9a28fdthhQT1272YXN5YCAKH7IbeFvc7sjmdd8/j3v/DCC95uBHmU7evc5uy+aV2I+RpymZVbMTHZIhDK1xoZ7uvcn59//vmg3k9+8pPoMdjNml3COaMTEMomuA1tPc4UF5PY2LKDDz44Wk+shKVO1n2fx1IqcwJLS1m+ZtuK3cJ5nKfatJFg2QEA7Lbbbt6+4447vD158uSg3gUXXOBtdo1PYdcnHnNsW8lSTOZrMz1ddtll3uZ9Dsu8gHCumDFjRkXnLkRLw/swS2pfmpJd8P1ESnKT2sNUQiozJh/bnivP3XY/xKEXeJ5qhKybqb0n/367l+V9C8/ZLDUDwjANjz76qLf5PsZiwzQwLJ1iOQ4QZlO9+eabvb3rrrsG9fr27Rs9fi2SasO33nrL22eddVZQj+W8nO1pypQpQT2WIb/22mveHjp0aFCPJW88j9hsyNUI11Cp7FKeNkIIIYQQQgghhBA5RA9thBBCCCGEEEIIIXKIHtoIIYQQQgghhBBC5JCaiGnTXM3oRRdd5O358+dH67FenGMBWN3ac889522OkWN1otttt523N99886CMj//73//e21Yffv/99wMIdXmtCacEtSl9Y6mdrSaPtfZWq8mw3tBec243jsth+wTHbeCylNaZ40PMmzcvKOPfaNuXY3s8++yz3j7llFOi31UvWO0mX2u2bdyL5tTjGC22XrXTbtYqNu1zCZvmuXfv3t6eOXNmUMaxiHi+sRp6rsftYeNQLV68uOz52Tbs0aNH2XMXcXgutulrOT4Kt5WdKzkWApOKkcN9oRRvrdE599xzg9d8jfbcc09v27hq7733nrdtTBu+5hx7r3379kE9TlXM7WbXKj4ex5WzcXY4Rh/H47Fjm8/DrtWNTGqPym1SabwNu87G5nkLz7GVpsm18PzA35vneCi8JwOAf//7395OXTtuNzv/xa5Dau+RStEd6yOpNNzcD2xab46xwfGqAODOO+/09lVXXRU9fj2SSqHO2D7D7TNy5Ehvn3jiiUG9G264YXVPMYDTUfPaAACDBg3yNq+7Nk5T6RjViK2SB+xYZPr06ePtW265JSiz62RTsfe6HCuK4wYdc8wxQT2OkdPceJ6VzvHytBFCCCGEEEIIIYTIIXpoI4QQQgghhBBCCJFDakIe1Vy3zA033NDbLLOxbnHsasYukTYlHLsnsvTHnh/LqDj9NxC6RC1cuNDb+++/f+RXtA1XXHGFt21KX3bFTKXN5utl3d1YXsbugcuWLQvqcXvw9bLH4+9i11hOAQcAd999t7eXL1/ubdsn+HO2jM9p7NixaCSsyy+ngGXXv5RrcModODbW5Yq/evD1t/Mau2/yXGilmTyueLyl5DKptk6lwRTl6dSpU7SM2ziVyptJSTL4NY9tXlcbGZvi9cknn/R2Sd4MAI899lhQj2W01113XVDGEqY333zT23bMxtrNSt94bPI4ty7/PNYvv/xyb9t5l9v+gQceCMp4r5OSQ9cjle5RrUt87HOVusrb/nPppZd620q+KyUlTcgTEyZM8DZLcgGgXbt23maJg12ruMzKj3jOS8meYlL6lFQxltbbHp/7ga3H+1c7TivtP/VIpWPR7m923333sraF74e4z1SaFt7W43tTu7ayRPaAAw4o+xkAmD17NoBwj9YIWDkUjx0ev5XOaSxrBsJ1nNvmmWeeCeqdd9553k7teVNlfB+cQp42QgghhBBCCCGEEDlED22EEEIIIYQQQgghcoge2gghhBBCCCGEEELkkLoWPnKMlVS8DY5ZwjEDrF6OU6yyPtxqTfm7bDwY/hzr295+++3yP6KN2GWXXbzNsWSAUGvPGnwb04bTnds0fDvuuKO3+TrYevya281q92Opom36NdaIbrHFFt7+8MMPg3r8XbZ9Ob3boYceikYiFR+Dr7Vtx9T4i8GxGqxm2/ZJkU732rVrV29PnDgx+jm+zvYYrP/nMn4fCOdT1nwvWbIkqNetW7cyv6L56W4bDRuDoVJicRes3joWT4Hn0Ebm/PPPD17z9eI1Yuuttw7qDR8+3NuXXHJJ9Pisw7fzXyyOhh0rsXg3dr3jFOK8NtsYSqz55zThQOPFsUkRi2FR6VzGKZsBYPz48d6+9957vW3nAE5Ze9xxx3n7r3/9a0XfC4RxMX71q195+8ILL6z4GK0B9+3UnoLjQdk1jdvJrjtcltoPxvY9zY3rF6tn97I8P9jflbf7iVqg0nZkuCzVpik4HtO6664blMX6oI1xVppXYmnl65VUjLBUHBse6zwnn3zyyUE9nmv5u/geGAjv820MVOa1117z9ve+972gjPfoKeRpI4QQQgghhBBCCJFD9NBGCCGEEEIIIYQQIofUhN95KiUeu6RZlzFOecjuxTbtH7uDcj1ObQ2EUiCWTllZEB/Puru999573u7Xr5+3rbtyKSW2PXZr8d3vfresDYSpBt944w1vX3/99UG9p59+2tvWdZp/O7tm25R1lUppmJhLIRC6E3N79u/fP6hn3ZMbGW5v66IbcwNvTrsBoVsquzBaN3AeF6m0naJAz549vW3bkMcct/Wmm24a1GM3Uk5PaNNUcj2ea+08LtnT6lFpatNKU8/aejyGucyui43KYYcdFrzmlN9jx471NqdqBYCvfe1r3l60aFFQ1qNHD2/zOLVyYHbHtuOZ4TG29tpre9u6jr///vveLqWPBYCrr746qMdlvL4DwMCBA8va9UoqjW9sbPJ+CQjd71944QVv2zTxvXv39jbLSm3aYpbwP/zww7FTT3LXXXd5+6WXXmrWMVqDcePGedvuG2Mpte3en6UMdg8ek1fYtuU9C5elJOJcz8qyYuPZvs9zAMvigPC+g9uQpY8iJCZvsu9zn0nNvan5geF+95e//CUoO+igg7x9/PHHe9veV5bOyfa5eqfSPZAldp34egPh3nbFihXethJxXvu7d+8elNl9QgneawPhPecdd9xR9jOAPG2EEEIIIYQQQgghcoke2gghhBBCCCGEEELkkJrwT7cuUOySxq5rd999d1Bv/vz53mb3QZvRiY/Brmpz5swJ6rG75Mcff+xt6+bPrsz2uziDCkeP5uwAwEqXyTxGA2eXsR122MHbNsPFyJEjvW3bkK8fX3PrKhpzY7PXhV/zZ/h7gLANWVbD2bJECLerbePmSjRKpKSPjHVDbdeunbclifpsWBqRynKQypoQyx5l5VGcDcFKVhnr0i6aRqVrg60Xy5xgxxi3MdtW0tOoTJ06NXjNY4yzLu20005Bveeff97bkyZNCsoqlZjGZG2pPpHK7sfny274AwYMCOr16tXL29YNfMstt4x+d95IZdrjecnKaZjU2seu9BdccIG37R6VpYadO3f2Nu+rgHBPydLgrbbaKqj3zjvvePuiiy6Knh+PYXtOZ599trdff/11b7PkDwAGDRoUPX5rwH3d9nte41JZZGLHs5/jtc+unzHZU6Xzc2pvzPscu5byPG5/Ix/jmmuu8XZTsojlmVTWoNYkld0rVs/CoTasrLQUJgMATj/9dG+/9dZbQb3S/UsjyKMqlZ2l5odK+wuvcSwhXrZsWVDv4IMPjh5jk0028TaPU87ECITzf4r6b2EhhBBCCCGEEEKIGkQPbYQQQgghhBBCCCFyiB7aCCGEEEIIIYQQQuSQmohpY+OcxHTGffv2DV5z/A3WBFvtIesNWetrY2Vw2mo+J5uOk2O02HgPrJHjFF/nnHNOUK+kg89DelWrB+Tfy21hdYKcjjJ1zSvVJVZDtxqLE8Bpxy2pWA9tpaVtTfg3pnS7Lfm9NjaRWJWUnpljl9j0oDyG7XzF8Bjhz9i4Xazh5fg2eZjL6onmxrSJpfJOxb5hLTanFW5kbFwBnhvnzp3rbY4XA6RTb3MqV24PGzcvFp8mld6dY6HY7+V9D5+fjaPBMVM4bgsALFiwwNucojovpGIcMKk4Ngyneb3//vuDMt7b8b5xm222Cepxu7777rvefu+994J6nGaY51GOeQGEfY3Txl555ZXR4/Xr1y8o47WWY7nY9OJtjU17zPD44L5u25b7QaV7m1SsqUpJpSHnccXj2caA4/XYnhMfk9uwXsjjvjsVt4ax8Uu33XZbbx933HFB2UMPPeTtESNGeNv2hdJ9ZaVzVy3TnLZvbqyfCRMmeLt///7e5ni5AHDXXXd5287dF198sbd5Pd13332bdU7ytBFCCCGEEEIIIYTIIXpoI4QQQgghhBBCCJFDqiaPirkZWrc9rsdui5W69qc44IADgtfsPsnuoKlUsywdsLIsdjNMuaHx+drfxddm4sSJ3ubUfnnDuqPFUij26dMneL3++ut7u1KJW8q9uzlucfZ7Ym2fuv62D1fqBlkvpNyGUymiq/mZVBuk0rc2EqnrwC6by5cvD8p4bly6dGn0+Dw3stSC3fqB+Ni2bThnzpyy9Sqd7xudlMSDr3WqXmp+5XHP403yqAL2urKcmvuwlZXw2LHjlNuNr39K4sbHsPVic61dB7msQ4cOiMGpTu2aPm/ePG/nUR7F/bvSNfzaa6/19vXXXx+ULVy40Ns2/TlL9bkv8GdS55faB3GbWqmrdc0vUUoJXGLYsGHR87j00ku9/Yc//MHbm266aVDv9ttvB9B20uVf/vKX3rZ7Un7N52dT9XK65UrlptWAx7ZdL7lv8rnbMAx8f8NzChBKHP/2t795Oy+psuuF2BppueKKK7xt++C3v/1tb992221BGffPAw880Nt2DS71oUZvz9h+xq5VsfAcdnxwiBVex5syV/ziF7/wNq+zRx11VMXHYBr3DkcIIYQQQgghhBAix+ihjRBCCCGEEEIIIUQOabYfeiobULXd25999tngNUfqf+6557zNLoFA6FrGbobWhYzPl49hf2Msqr49XipLCrslc70HHnggqHfwwQdHj9HWsIsXtzvLLIDQtcxGsGf3VXb7TLlvprI/xGQ2NgMYu5HyMRpN8tQUUn091j72esbc/lOkpBuxrAq2vRuJlDSM3ehtBpMePXp4m8eHvZbs2s8u3dZtnj/H7vqdO3cO6nEmGlEZ06dP97aVuPCYSMkOK82iE8tetGTJkspOts5JSb95LNqMbJxtLSWPSrm6x6Q0tj1jMhHrLs7fy9nf7BzA87o9xvvvvx8937Zg3LhxwevHH3/c29OmTfO23ZuwzIt/k80w2a1bN29biShfa1vG8H6Tr2dK5sb7Jdt/eA/GbffSSy8F9Xgu5mynANC1a1dvb7HFFt62Epwbb7wRQJghsDWZMWOGt3mvCYTXn+dJu1bF9oOtSWrMcv+z8qjUfM97rJ49e5b9jFh9UrLhn/70p97msb3xxhsH9fh+dvPNNw/KuM15XqqlLFHcvyuVbdt5bXXDHtjPx8bB4MGDg9d77rmntzl7Vwq7L+OxyPNPSoacQp42QgghhBBCCCGEEDlED22EEEIIIYQQQgghcoge2gghhBBCCCGEEELkkGYHn6k0BohNb8a6PNbn8/tAGOOF6wGhfpU1cjaWDKev7dKli7etTpt1gxy3wepkWf/KKRStlnvUqFHetlo6Ti3N2tUXX3wRtUJMD5jSIVYaCyX1XdWI08DnxFrDlGay0XXAqWtbaUr21dWLV5reWJSH56Q+ffoEZayz5bnRpirmeW7FihXetrHEWG9t53WG59pFixZ522q+ldJ9JVOnTvU2x9QAwutu408wqVTSsXq8Fi5YsCCoN3r0aG/b1MKNBMct4LmwU6dOQb1U2zCxGDn2u3h82LkwFn8uFVeM2zoVO87GLqw0VllLsmjRIvz+978HsGqcQI4lxL/Lxofg/SDvKe21+OCDD7xt24fnRI6FY+MAcT/h2Dr2uzhGC7cB/yZ7DI6twPtOIOwLNuYS70v5+HmIWcRx0PjcbHwIHmP8e2w7pfaAsb2NvfexMSxi8PH5GKk0wxwPyfZTXqtt2/DYnDNnTkXnlwdSsVJb87u5TWz78tjm9ficc84J6nE8qLlz53r7qquuCuql9s3jx4/3Nsdw2nnnnaOfaSkqjTeautdrq9ihqX3j4Ycf7u3+/fsHZX/+85/Lfsaus3x8O8dzzLCBAwd+9sl+Bo29AxZCCCGEEEIIIYTIKXpoI4QQQgghhBBCCJFDmi2PeuGFF4LXF198sbc5BSC70QOhGxG7GNl0iuxGZd30Y+67NuU0u2rffffd3t5+++2DepyWll0ObQo3ZuLEid5mN1kgdFu3ki1222S3qdR31SosjbDtG3OlTslvmkMqnSKXWZc2sZJquL1XKoeLuVna9uFzauS2i0mH2B0XAF577TVv9+7dOyhbvny5t1lSutlmmwX1eL5iV13rXs/zaYp1113X23feeae3zzrrrKBeo0uimCeffNLb1g05JpOp1O3fEks/bfvF9ddf7+1GkkdVKpu14yM1X3FbxVKuA/G2SZ0TH8PO6fxdLDux6zbLdCw2dXZb0L59e5x00kkAVt3nPf/8896ePHmyt2fPnh3UY6kJz4025TJfT+suz3LPJUuWeDslz2EZhv2u2JrJcygQ7jdZTmNlCdxPbLgAPg+WgthwAV/96lcBAH//+9/LnltLwDJfJiVZ4j2f/a0cvsHKj2KyjtQYq4aUnq8zt6ftO3zfYecU/p21JB9PyWcqDaNQje/mcWnl3yzR+81vfuPtvfbaK6j30ksvefvee+9t1jnx70qdU2tgr3Gl+4gYr7/+evD65ptv9raVmnXs2LHsMVIyJV6P7Li/8MILvc3PK6ykNkZqT2rL+BxtaAKm0ntd7YaFEEIIIYQQQgghcoge2gghhBBCCCGEEELkkCbLo0ouWmeeeWbwPkth2G3UugpZuVAJ63bLUicre2I4urp1cz3//PPLHoPduQGgc+fO3mY3Kuvuxq5Nb7zxhrdZUgCE7pjWbTGWfcFmTMkzlbrCpVwd2X015hJuX6fc8WJlNvI7u56m5DeMsketvLYpN2uul3LJrdS9uNJsYTwHrL/++tHP1CMxN80RI0YEr7/85S9728oY+JrxHNq1a9egHruzcj+wWYxYOrrJJpt4286TLBthl2OeWwFg8803hyjAWQatZIbnsEqzQqXg8cd9xsokOHuU+Gz4Wtr5NDaHNnc+jcnRbRuyjJ3lUXbscTYTKydZXSlztSidR9++fYP3d9xxx7L17d5z5syZ3n7zzTe9bSXsvOe1c2qsHe183b59e29zGAB+HwhlapwJysrXWDaRklDwvijVbpyVye7dS/3OSrRaEt5bM7Y/8zXn32fDNfBcmJIgxsaRfR2T31v4e+34jcm57G9n6Z6d0+1vqQeqvQ9PZUNK3bv89Kc/9TZnJeZ9DxCG5Ggu3O9YZmnn3pYiyzIv1bTXi8+N+xtLjwDgpptu8rbNpMjwvGsll9OmTSv7mZT0m8eODRfAcrWHH344ek68FvIzhJQsi8clEParIUOGRL9L8ighhBBCCCGEEEKIGkYPbYQQQgghhBBCCCFyiB7aCCGEEEIIIYQQQuSQJgkflyxZgr/85S8AVo0fw2lkOTUsp08EVo1rUMLGFOE4FTZmAsdaYM0Zx08AgFNOOcXbf/vb37x98MEHB/VYS8fnPnbs2KDeU0895W3WkFo9LWukbUwVhnWAtl5Jg5f6fN7h62I1gKwZTWm+Y3FnrL43lhrP6mBjWl+rdRYr4fSjth1jOsyUXrg52Hbj4+Uh1WzesPrq/v37e9u2Ic8xqZS+sbhPqTGb0hVzLJ1YXB1AMW0YjqthU0lXmoIzNT/G4D7Day4ALFiwwNvcf+y6WG9wDBIgTMGbikHD18+uY7wuptKKxmKJ2Xk3NmZT8Wj43Hv06BHUGzNmjLdt+1YaK6klWXPNNX2cF97LAcD8+fO9nYofsNFGG3l76NCh3rbrTCy+ChAfY7Zf8DFj6b+BcA3mz3CfA8L0tbz3tinEU3EXP/roI29zH7dr8Kabbgpg1fmgJdljjz3Kvm/nsVi8DXv9+Tqk4uKk4h/ya457Ya95LKW0PR6fE9ez8wEfPxWDpZZI7Rt5j75w4cKgHo9tHrMpKl37/vd//zd4zf2J91nDhg2r6Hip+Jl2jHH7c0yb1sI5l5znyjFu3LjgNbdVai7kuK6LFi0Kyh588EFv2/t3e77lOO6444LX+++/v7dTabhT8XRj8H4ICGOB7bLLLk0+nkWeNkIIIYQQQgghhBA5RA9thBBCCCGEEEIIIXJIk+RRn//8570Lk5UssSsmuxla99qYy+Z7770X1GMX1ZIbZrljsPs920DoMnjYYYd5u1+/fkE9djln+ZZ1l+T0iuwyZl0T2fXYurnG0ltbt8Dp06cDSMsV8k6lLpvNcetPpTSMuY7bMm7DlItvo6f8TqUSbo7UolJSbVxpas1GgmWenTt3DsrYpd6maOX25TGbGhPcNtZtOzZn2RS07EbKkld28W90bPpIvjbsTgyE1z2VopbHaWw9sq/52Pvtt19Q75577vE2S4qr4QqcN3g9t/MdXy+W+1l431PpOpaSWlSaWjglvYrJP3r27BnU4/NIyTXygE1RbV/H4HkvJUFhaZKd82LXwkrIeGym9kuxddZK9Hge5X5hJRmp3xXrM/b6ldIdN0dC0Fz+8Y9/lH3fyv34Nc+ZNoQC17PjKNbX7fWKhQGw8wNf11Q9biu+p7HzeKydyr2uFVJ7yNdee83bKak1y/uAVfcdlfDOO+94e/To0UEZ76VGjRrV5GOnpHypunPmzGnyd60uH3zwAZ599tmy33/kkUd6m/spS9Us7dq187aVd/M8YueaM88809speRRzyCGHeHvKlClBmU0pXk04tAtQef9Tym8hhBBCCCGEEEKIGkYPbYQQQgghhBBCCCFySJPlUSVZlHXp6t69u7c5ar91dWeJUceOHcvaQOgiaF1PuSwVSZ/dB9u3b+9tdrMDQrkAy7ms+xZ/F5+vdXFm6YYtY9dblgew2xgAjB8/HsCqrn61RKXZJCqV1VTqPpaKUM5l7EJay9e5pUllMItJLVLZU5qD7SM8xmyWkEaFXYbt/Mxzpm1Pntd4vkrJHVi2k8p4wN/bq1evoN4bb7xR9jPWvXTZsmXeZtlsI/Dqq69Gy1JrS2oscntzX7Au9TEp6bRp04J63HZTp071dj3Ko/ia2DmJxwvLVCwpSWnMVT4lXWPbfj7WD1JZFVl+bjO3peRR9SJTZTf9lPTH7g9Fy/Poo4+Wfd/OhSxZ4v58/fXXB/VOOOEEb9v1ju8LuK9bKRaXVSoX58/YrGT8mtdCmzmLsyzyfVUKm3XJysWqTWlOaO4enz/XmuvJt771LW+XQlWUeOihh1br2HadTc2b3E9ef/311fre5vDxxx9jxowZAIDTTz89KLvooou8zWOFpWW2jPcRVuLGn7NrC69d5557rre/+c1vBvXOO+88b3O253322Seox88Dqo2Vh1kJa4xKx4g8bYQQQgghhBBCCCFyiB7aCCGEEEIIIYQQQuQQPbQRQgghhBBCCCGEyCFNimmz9tprY8CAAQDCFNoA8Oc//9nbpVSAANCnT5+gHqcG4xg0Ns5CLO0iEE+JZ9MaskaM027ZdLixdH72eKwbjaU4t/Ws1pT1sKzv43S9wEqtKdfJC81J7dzcGCcxvWcqXk4q5TefB+ugK42/04ik0tzyNax2TINUOlQeF2+99Za3Bw4cWNVzqCV4vrLjjec/G7+J51een+w153mS579UPAHWKQ8ePDioV0olCYRzsp13OX5Oo8W0sfr5Dh06eNuuDbGUzjbWWywVsK3HaVS5jTkWm/3eSZMmlfkV9YmdC3nM8R7IwmtNKm12LEVwuc+VSMWEqDRNOMfR2GabbYIyPg97TvUS00bkF45vybEi7JoWGx/2vuUHP/iBt++8886gjNc4jqtm7x9szM0SlcYIs/Muf27HHXf0Nqc9BoBnnnmm7LHLfXeJ4cOHB685dktL0NR7hVR9nl8OPPDAoIz3Geeff35Qdvzxx1f03Zdccom3OXbSWWedFdTr169fRcerBrwG8D6otWjfvj1OPfVUAMCf/vSnoIxjw/K52bHXqVMnb3NfX7FiRVCP9zY2zhO3/ZVXXlnWBsJYsxyP7Gc/+xli8DqWSr9eKfZ3VRpvqtLvlqeNEEIIIYQQQgghRA7RQxshhBBCCCGEEEKIHNIkeRRzwQUXBK9LsikA+PWvf+1tK/th9yV2G2L3fSB0WbLuh7GUsinX4FTKW5ZipY7HcJk995hbJRC6QLGbef/+/YN6J554IgDgmmuuiZ5DW8G/PeXOyFILvsYpUqne2OWzUndxe3587ny8SuVWjci8efOiZbE0srY9Kk2FGXNVtGOR5RrsVtnILF261Nt2juN5d/LkyUEZj8127dpFj8HXPCVtZcnqxIkTvf3Vr341qMfzPx/DugFbuVQjwdI/IFxbrEyJxxintLT1HnzwQW8fdNBB3rbpjVlywGk7LVxvypQp0Xr1Rmpt2XTTTaOfY/kgj0sglHzEJA5AOBZTkqXY+dk9Fbuj89hOpS6359fI41S0DjzmeC6sVIJgufzyy8vaKax0g88jtTfm17w3Zhlqc7H7I5ZZ8nrMcz/QsvKo999/H08//TSAVdOk8zzHkud11lknqMdzJf8OtgHgzTff9PZVV10VlHG654033tjbjz32WFDvt7/9rbeHDh3q7Ur7RXOpdD9sw3C0Nj179gxev/jii97u0aOHt+1+kNPM8++xewpek1LXZMMNN/R26pqwLCslaWvO/Z1dP3nvxPJiYGWoE4udR2yfjiFPGyGEEEIIIYQQQogcooc2QgghhBBCCCGEEDlED22EEEIIIYQQQgghckiTY9qUNGk2ZgWnYGN75MiRQT2OhTNr1ixvWx0YazRtvBHWa6ZSDrN+kXVr3bp1C+qxlox1dpWmgbZ6TY5xYzXm++67r7e33nprb++yyy4VfVetYq9DLP2orRfT69v+F4s/lErLyijldxweHzz2gPD68jW07VFp/CBOhZlKjctxF1hP28gsXrzY27afc4wTm5KQrzOnKrbaZNYSs/Y8FUeDsRpmPh73I6trnz9/vre33HLLir6rXuCYMwB8jABg1THGY8SmwGVi8Wls6nabUjxWj+eH1kyH2hbwNU/FvOOYDRbWw1tdO19zjlFlr3ml6bsZHqc2jsaHH37obR5vVmfP525j2Nj5Qohq83//93/efuCBB7zN/Reofhpfxo6JSmNRVBuOMcJrPxDG+OE5Ztddd23p0/L8+9//9vd4fK8HAIsWLfI2xwSyaw7vEXjO6969e1CvFAMUWDU+6BNPPOHt0aNHe3vSpElBvSFDhnib4+LY+zueA1s6zgzHSvnKV77Sot/1Wfz4xz8OXv/1r3/19ty5c71t1yNeC3ndsdcuFoMWCO87UveL3JfuvPPOMr9i1c81Z35Irbl2TY/FtKl032yRp40QQgghhBBCCCFEDtFDGyGEEEIIIYQQQogc0mR5VFNdifbaa6/gNacJY15//fXgNbv7sYscALz99tve5tSa1o2tT58+TTpXURmVpkhjqcUbb7wRlLG7N/cp27/Y5ZrLUukU+dhWzhNDKb/j7LDDDt6ePn16UMZSm5SbMLsTcvtUem3ZZR8I+0KjSWZisIs4SzSBVdNoM+zOyXOodVHlOZlTFVvXdK7Htk1fHUvpbvsEu7w2GjYl62mnneZt66LLErhUuujYGt6hQ4fgNY9t7hfvvfdeUI9fn3nmmdHvrQd4nbD7jUolS0ceeaS37bXkccXflWrPlCyVX3O7W7lVu3btvD148ODod7F8wZ6TJMaipWHZz+zZs71twwvwuDr++ONX+3tjMn37OrWfiZXZ+bjSdXH//ff39k033RSUsXz8q1/9qrfPO++86PlVm/bt2+PUU09t0mdYEgqE93rLli0r+z4QXifuF0AoieJ+wWE8gLCfWPkV05qpt1ke9Zvf/MbbF110UaudQwkrfeZr/uijj3r74osvDuq98sor3rbrXbXZbbfdvL3nnnu22PeknoNwfwPC+2CmufeV8rQRQgghhBBCCCGEyCF6aCOEEEIIIYQQQgiRQ5osj2opttpqq+Rrpm/fvi19OqIKsHs9u2sCoWyJXSKtizW7nlYqdWLXb3s8zhz2z3/+09tWuhE7B6D62QjyDkttTj755KDsqaee8vaSJUu8bSUzLLWJZaUBwvbiduRMCUAou7RSoEaFJYi9evUKymxEe4b7N2cdsnI3dkHnyPxWRrX33nuXPbYdRzw/cBv27t07qNeSbq61xsSJE71ts2QwKRduztzBLFiwIHjNfYbHpZWrjRgxwtssV65HeM1IySRshjbGZuGoJdilOzWehWhpOGukzVzGc5SV0jC8T7FZC5mUhL8lie2HAGDAgAHRMt5vn3HGGS1zci0AS3zLvW40eN+b53ZkqR7bFg6vMHbs2KCM9zbvvPNOUMbSOF6DunbtGtS74YYbyn5vKtNmc0jtr84999zgdSx8g5VXV0pj3X0KIYQQQgghhBBC1Ah6aCOEEEIIIYQQQgiRQ/TQRgghhBBCCCGEECKH5CamjagdUmkIme22287b22yzTVDGqRtTsWpYN7/uuutGvzeWUtpqFzmeCmvwOa21pdFi2Fj42to4JwcccEDZz7AGFQjjZbz77rvetu3YqVOnsnal6cQbOT37dddd522rcedxdMwxxwRlHM+JY5LMnTs3qMdxclJpgZkjjjgiWnbUUUdVdAyxEk67aXXao0aN8vbUqVO9PXLkyKDerrvuWvbYVjPPsW+4z9hUqY3ERhtt5O0tttgiKOM0sTvuuGP0GKl04Hmfvzgt7syZM4OyQYMGtfbpiAaGx9GVV14ZlPE47dy5c/QYrZm+uTmk5oOOHTt6m1NDA+HvavT9a73w85//vK1PYbXhNdOun8cdd1yLfW+119XU8fbZZ5+KjrHmmms267s1moUQQgghhBBCCCFyiB7aCCGEEEIIIYQQQuQQl3LVXaWyc4sBzG650xFl2DTLso6fXa0y1IZthtqx9lEb1gdqx9pHbVgfqB1rH7VhfaB2rH3UhvVBtB2b9NBGCCGEEEIIIYQQQrQOkkcJIYQQQgghhBBC5BA9tBFCCCGEEEIIIYTIIXpoI4QQQgghhBBCCJFDcvHQxjl3qHMuc85tVWH9Wc65DmXe/6CJ39uk+onjnOqc61KNY9Uqzrn2zrnxxX8LnHPv0OsvJD7X0zk3OVJ2iXOubNL7ctfcOXesc+4nzrmhzrldVu8XNR5qw8bEOfdpsY2nOOcmOOd+5JzLxdrQqGgs1jc05iY75+51zq39GfWfds4NLtpl9z+i9SmOjynOuYnF9tyxisce6px7qFrHE+XRWKw/WmJccruvTh1ROWrHVflcW59AkeMAPFf8/3/b+Fyaw6kAJgOY18bn0WZkWbYUwAAAcM79FMAHWZb9ejWPeXG5951za6L8NT8AwLUADgbwAYDRq/P9jYbasGH5Z5ZlAwDAObcxgDsBrA8zFzvnPpdl2X9a//QaD43FuofH3B0Avg3gN216RoVzcSgkqPhvW59L3nHO7QzgIADbZVn2cfHmPfpAtTXRXN0kNBbriDyPS1E5asfytPlfU51z6wIYAuD/ATiW3h9afNp1n3PudefcHcVJjD+7lnPuEefct8oc9xzn3CvFJ3Q/S3z/1cUneU865zoW3xvgnHux+NlhzrkNY+87544EMBjAHcUngWtV5cLUIc65bZxzLxev00Tn3ObFojWdczcW2+Gx0jV0zt1SvL6lv2hc4Zwbh8LDveCaF/vGAADLUFh0f1gs26341+eRxe980jnXg45/g3NujHNuunPuoFa+JDWH2rC+ybJsEYDTAJzhCpzqnBvunBsJ4Enn3DrOuZuLfeBV59whQPl+Uaz7D1fw3pnsnDumTX9cnaGxWDeMArCZM54VzrnfO+dOTX3QOXd2cWxNds6dVXzvcufc96jOT51z/1O0V9kXFdtzmnPuVhQe3HWv+i+sTzoDWJJl2ccAkGXZkizL5hXH1s+cc+Occ5Nc0YM8MXf2dM6NKtYf58p4tTnnti9+po9zbpBz7hnn3Fjn3AjnXOdinaedc9c458YAOLP1LkNdobFY+8TG5cXF6z3ZOfen4hpXGjdXFMfldOfcbsX313LO3eWcm+qcGwbA39s5564vrnNTXOL+UqwWascytPlDGwCHAHg0y7LpAJY65wZR2UAAZwH4MoDeAHalsnUBPAjgr1mW3cgHdM7tB2BzADugsPEc5Jzbvcx3rwNgTJZl2wB4Biv/snwrgPOyLOsPYFLq/SzL7gMwBsAJWZYNyLLsn02/BA3DtwH8tvhXjcEA3i6+vzmAPxTbYQWAIyKfX5pl2XZZlt2OVa/5QAATsiybCeAGAFcXy0YB+B2AvxTb7Q4U/npcoicK/eSrAG5wzn2par+2PlEb1jlZls0AsCaAjYtvbQfgyCzL9gDwEwAjsyzbAcCeAK50zq2D8v1ifwDzsizbNsuyvgAebd1fUvdoLNY4zrnPoeDRNKkZnx0E4OsAdgSwE4BvOecGArgbwNFU9WgAd3/GvmhzANdlWbZNlmWzm/lzGo3HAHQv3iBc55zbg8qWZFm2HYDrAfxP8b3Y3LkIwL7F+scgHE8oPsS5AYW98hwUxt+RWZYNAnAzgF9Q9S9kWTY4y7Krqv1j6x2NxbohNi5/n2XZ9sW9yFooeHGU+FxxXJ6Flfd73wHwUZZlWxff43vTn2RZNhhAfwB7OOf6t+DvaVTUjmXIw0Ob4wDcVbTvKr4u8XKWZW8X3QPHo7AhLPF3AH/OsuzWMsfcr/jvVQDjAGyFwkRo+S8KkyoA3A5giHOuHYANsix7pvj+XwDsHnu/0h8pAAAvALjAOXcegE3pAdfMLMvGF+2xCNuZuTvyPlC4QXwkUrYzCpIPALgNBc+uEvdkWfbfLMveADADhb4i4qgNG4/HsyxbVrT3A3C+c248gKcBfAlAD5TvF5MA7Fv868duWZa92/qnXtdoLNYuaxXH0BgUbsT/rxnHGAJgWJZlH2ZZ9gGABwDslmXZqwA2ds51cc5tC2B5lmVzkd4Xzc6y7MXV+kUNRvGaD0LBM3ExCjfjpxaLHyj+z+MvNnd+HsCNzrlJAO5F4Y+UJbYG8CcAB2dZNgfAlgD6Ani8eJwLAXSj+qkxLcqjsVhHJMblns65l4rjbC8A29DHyo3X3VG4L0SWZRMBTKT6R7uCl+qrxePwmBVVQO1YnjaNaeOc2wiFi97POZeh8NfdzDl3TrHKx1T9U4Tn+zyA/Z1zd2ZZltlDA7gsy7I/NvGU7HHEauCcOwwrn3Z+M8uyO51zL6HwV9iHnXOno7Cxt+0ck5h9mPi6/RD/i3IK2+bqA4TasPFwzvVGoQ0XFd/iNnMAjsiybJr52FTbL7IsG+mc2w7AgQAudc49mWXZJS19/vWKxmJd4eNolHDO/QfhH9JWx0vpXgBHAuiElTfyZfdFzrmeSPcFESHLsk9ReADzdPEm4pRiUWkM8r617NzpCjGrFgLYFoX2/xcVz0ehHwxEId6UAzAly7KdI6ekdmw6Got1RplxeToK3hSDsyybWxxz3KblxmtZnHO9UPCe2z7LsuXOuVuwev1DRFA7rkpbe9ocCeC2LMs2zbKsZ5Zl3QHMBLBbBZ+9GMByAH8oUzYCwDdcIV4OnHNdXSHApmWN4jkAwPEAniv+NXh5SQ8H4CQAz8TeL9rvA1ivgnNuKLIsG1Z0qR+QZdmY4s3gjCzLrkXBU2p1XNH8NS96QX0uKwTvDMqKjMbKeEknoKBbLnGUc24N51wfFCR49ma0oVEbNhauENfrBhRcUMvdcI8A8H3SEQ8s/r9Kv3CFbEQfFeU3V6IgsxLNRGOx7pkN4MvOuS865zYAsPdn1B8F4FDn3NpFmc1hWNkWd6PQRkeicNMIVL4vEhXgnNvSrYwjBRRkLik5S9m5E0A7APOLHuUnofDHyxIrUHgoe5lzbigK46mjKwTphHPu8845/kuzqA4aizVKZFyW1qElxWt+5CofXJVnUbgvhHOuL1aur+uj8GDtXefcJihI6kSVUTuWp62zRx0H4Arz3v3F9ytx8zwTwM3OuV9lWXZu6c0syx5zzm0N4IXi+vgBgBOx8i/HJT4EsINz7sJiWSlQ5iko6PHXRuEvl1//jPdvKb7/TwA7Z4prE+NoACc55z4BsADAL1EYOM3hFqy85lcBeILKHgRwnysE+vt+8d+fix5ci7Gy3YCCO+zLxfP4dpZl/FcusSpqw/qj5B7+eQD/QUHyEsue8XMA1wCY6AppwWeioCku1y+2RyFuw38BfIKCtlhUD43FOqL4l8N7UAhAOhMFl+1U/XHFvw6+XHzrpqIcA1mWTXHOrQfgnSzL5hffi+2LPm2J39MArAvgd8Wb+v8AeBMFV/5YAO7Y3HkdgPudcyejEPcr8LTIsmyhKwT1fgTAN1C4Ubm29HC1eMwp1fxhjY7GYk0TG5crUGjPBQBeqeA416Owzk0FMBUFyQ2yLJvgnHsVwOsA5qKg+hDVR+1YBlf+j6lC1A7OuZtQWCSbpAMuLrIPZYVg0qINURsKkQ80FoUQQggh8kVbe9oIsdpkWfbNtj4HsXqoDYXIBxqLQgghhBD5Qp42QgghhBBCCCGEEDmkrQMRCyGEEEIIIYQQQogy6KGNEEIIIYQQQgghRA7RQxshhBBCCCGEEEKIHNKkQMQdOnTIevbs2SIn8t///jd4/c4773j7ww+DDIho3769tzt27Ngi5wMAy5cvD14vWbLE2+uvvzKz6iabbNJi5zBr1iwsWbLEVet4LdmGLc2//rUye+x7770XlK255preXmONlc8i11133aDe5z//+RY6uzRjx45dkmVZ1TprLbdjraKxWB9oLNY+Gov1Qb2MRbtHXbp0qbc/97mV22zepwBAMd0zAOA///lP9Phf+MIXvP3RRx9FP/PJJ594e8stt/ys064KjTAW+Tqn2rCWqeWxyLFZ//3vfwdl//znP729zjrreLsa9wL8Xfw9ANCuXbvVPn5TqdexyM8H+Drba7722mt7m8clz4tA2PZrrbVW1c6zWqTGYpMe2vTs2RNjxoypzlkZ7KJ30UUXeXv06NFB2cknn+zt7373uy1yPgBw7733Bq9vuukmbx9wwAHePuuss1rsHAYPHlzV47VkG7Y006ZN8/ajjz4alG200Ube/tKXvuTtXXbZJajXtWvX1T4PXiAqXbCdc7NX+4uJWm7HWkVjsT7QWKx9NBbrg3oZi6+88krw+tZbb/U2/5FxvfXWC+rxAx3+o6DdV/To0cPb48eP9/aiRYuCeosXL/b2U089VcmprzaNMBb5IZy9Gec2bEls0hh+zX+obC5tNRb5htz+jlQZww9P5syZE5RNmTLF2zvuuKO3O3Xq9Jnn9lnMnr3ykr322mtB2f777+/tSu8TrPNCU9u1lsZiU37rBx984G1uT7YBoH///t7+4he/6O358+cH9djRYtttt41+b3Pu9apBaixKHiWEEEIIIYQQQgiRQ1rnEXGEb3/7295+5plngjJ+CmflR+yFc+2113q7e/fuQb3NN9/c2/x0fNmyZUE99uThJ7ZWgtO5c2dvX3/99d5+8MEHg3o33nijt3v37g2RptKnmd/5zne8/fLLLwdl7L768ccfR4/xzW9+09sTJkzwNrscA8Duu+/u7auuuiooY3e6Tz/91NvWbVYIIYQQ9c3TTz8dvJ48ebK3eU8zc+bMoB7/BZk9bTbccMOgHu9fN9hgA2936NAhqDdr1qyKz1mEe88RI0YEZffcc4+32Wtp4cKFQT2W7fM9zauvvhrU43uaqVOnenurrbYK6rFHP3sO2L0xv7ZeOLUk2eJzrdT74vTTTw9e856fPSyAsL1++9vflv1eIJTQDBw40NtWgsOeVexdY73oWA2wYsUKb3/ta18L6h1xxBHebq6nUS2S+j2sqgCA999/39vTp0/39sSJE4N6PE/yHMrXHwjHLI+dAQMGBPXyOI7qqxcIIYQQQgghhBBC1Al6aCOEEEIIIYQQQgiRQ/TQRgghhBBCCCGEECKHtHpMm5EjR3qb9b2sIQTCeDJW58jRnjla/ltvvRXU44xUHFXb6uBYo8gaYXtOHKm/V69e3rZ6uR/96EfeHjZsGESaSmPaLFiwwNtW882xiDg9pm2b22+/3dusa7Tp/zgquc0OwHGU+HvzmDpOCCGEEC2HzX7K+0OOoWjjLvLellN027h8XI9j2nDWTPs5jm+Th7S9bQVn+Dn66KODMm63d999NyjjmBt8nTlttD0+xzay8YsYvh/heB0AcOyxx3qb96innXZaUO/888/3tt03t1XWm+ZQaRasH//4x95evnx5UNalSxdv25TfPOa4jW1GIb7uHD9z5513DupxjFX+XhtfimPkcCpqjpUEhNmufvjDHwZlNlZRPcP372+//XZQtummm3qb283Ok9w2POfZeKOc0Y/vEW2mrGpn46oG8rQRQgghhBBCCCGEyCF6aCOEEEIIIYQQQgiRQ1pdHvX44497m92XrJsTy1XYzQwI3dBYumJdyTgdM8tdrIxl3XXX9TanbXvnnXeCeuzixt/VrVu3oB5Lu5577rmgbMiQIRAhqbR27OrIboTWRZVTfrPLK7ctEMqq2H2VJVVA2L7WZZGptzR8QgghhKgcTkMLhLJ9TuttZVT8euONN/Y272eAcA/Mchq75+XPPfvss95uZHnUqaee6m0rq+H9oN1T8t6OJUZWms9SOE7bvvfeewf11l9/fW/zPYLdo8akTQ8//HBQb/jw4d4ePXp0UJZ3SRST2v/PmDHD25MnT/a2lRny/aP97XzMrl27lv0MEN5f3Hvvvd7m+z4gvP/kNuX7Tfu9bLOkCgAmTZoUPQbLerjMyn3qAZYpscwJCNO48/32bbfdFtTjcCQHHnigt/fZZ5+g3tZbb132u1hSCoTp3vMS/kJ3nEIIIYQQQgghhBA5RA9thBBCCCGEEEIIIXJIq8uj5s2b5212LUvJo6zLGNdlWYt1M7RRxEtY1zJ2Vfzoo4+8bd3i+Pjs7mbPj93zJI8qD7uA2uxgDGcbY1dilrGljmH7AB+D+5GV4PXv37/sZ4Awi1WnTp2i5yDplBBCCFHfsCwGCCVMqQxFnJWI95FW9sTH4P2l3TezPMpKgRqJG2+80dsLFy70ts3ww9c8tV/j9rB7Rb5nYAmF3TdyW6WkLvz6S1/6krc7duwY1OP7lvvvvz8oO+KII8r8inxis7MyTz75pLe5ffiaA+F1stJChsdl586dgzKWND744IPeHjBgQFCP5Y4sn7H9h+9h+d7Ajm3uT6NGjQrKhg4dGv1cLcLXgaVvQHhdx48fH5SxHI4lbm+++WZQj58H8L0fP3cAQjkhy+JsBmqWYh133HFl329tdFcphBBCCCGEEEIIkUP00EYIIYQQQgghhBAih+ihjRBCCCGEEEIIIUQOafGYNjbOB+sw27VrV9YGgH/961/RY7IGkLWhrIkDQm0ja92sJpXPkT9jz50/xxpKC2uObSpIUYCvkU23zbzyyive5vgxG2ywQVBv2rRpZY9t4xKxbpXh+EoAcMghh3j7scceC8oGDRpU9pzqQXMqhBCiduD0tDfccENQts0223ibUxDz+iZWHxurhuNlcKyL1157LajHcWdSe8rY3sLub7ie/a5G4rrrrvM2XxMbf5LhGCT2c4xNKR2rZ2O18HfzntfW4/TGqfsRjn1jUx/XUkybFNyH+TrbeEF8PVMxMvma2XiXfN05fmmqHt8T2pg2PJ55frD3ttyfOK05EMa0ScX+qRU4jg3HkgHCuWyzzTYLyiZOnOjtHXbYwdt8/wWEKbs5PhB/BgBefvllb3O8nL322iuox/3l+eef9/YWW2wR1Bs4cCBaC3naCCGEEEIIIYQQQuQQPbQRQgghhBBCCCGEyCEt7m81c+bM4DW7rnG6NCtP2XDDDb1t5Uycto1dxqwbG7vTsUuadWdkt0h2R7T12I2NXeGsiyrzzjvvRMsaGb621t2Ueeqpp8q+b+VR++67r7fZBc8em+VRnMrPppjjvmRdTTfddNOy52RTN4rPht0ZAeDtt9/29pAhQ1r5bIQQorZ48cUXvc17IyCUF//ud7/z9plnnhnUu+aaa5r8vVaicOmll3qb0yz/8Y9/DOpZGUqtwimcWfYPhLI0lm7Y/ciKFSu8zXtFK/Xn/THvN20K60022cTb8+fPT55/o8D3HPYegdswJTVLpQOP3RfYtuZ7FS6zKar5fFliw+nEbT27j+IUx126dImee97hFMx8/ew9Id9LWpkhjz9uH3t/x+3A32Xr8T6f61n5Evct/l4+V3v8WPiGeoHnu4033jhaZsfOfvvt522eCzk1u63HckQre+K24nZftmxZUG+dddbxNvc5O7duvvnm3mZpXUsgTxshhBBCCCGEEEKIHKKHNkIIIYQQQgghhBA5pMXlUdaNiCNvp1zVWIJiI76vt956ZT9nXUrZDZe/y7rnstsTuyBauQu72XF2AOsmzOfRvn37oIzd3zp27IhGhds0FRWdpU4fffSRt9klHAA22mgjb3OfsFnJOBo7S3GOO+64oN4vf/nL6DlVKu0S5eFsJxdddFFQtv/++3ubJXB9+/Zt0XO6/fbbvW0jw9vI80II0dLwGpmS3nJWC7vesVyK177f/va3Qb2TTjrJ25wd0cIu7Hw8AFi6dKm3ea0+5ZRTgnp77LFH9Pi1BLvS854UCPd2vOez8hy+TryXsBKKXXfd1du8l7X9gqUhjZTN8hvf+Ebwmq8fX+O5c+cG9VhqYTPR8L0Kt5vd86XaIwbXS2W04nuOBQsWBGVLlizxtu1/zzzzjLft3jbPWNkTS01YgmgzzrK0kLMBAeGYiGUKtthxyrDsqdL25vAcVoLD58v3O/UCj0W+rva+j6VIdv7jOZSvpQ1VwW3K+/auXbsG9aZMmeJtfh5gM49Vmlma7yW32mortCTytBFCCCGEEEIIIYTIIXpoI4QQQgghhBBCCJFD9NBGCCGEEEIIIYQQIoe0eEwb1jkDYSyYd99919vPPvtsUO+EE07wtk1Zx3FyWF9oU+JZ3WMJq6XjerG0b0CYooxjqth0gFtvvbW3bSrI119/3duNHNMmpgUdNWpU8HrRokXe5rgmtl8tX77c25wu3qbQY93ym2++6W1uM1E5rO3kcWBT3f/gBz8oW9a7d++g3sSJE7192mmneXv06NEVnY+Na3XzzTd7mzXgQKibZe10LafIrAQb66DSuEzXXnutt7fbbjtv29SNPDfyHNe/f/+gntUZry6XXXaZtznlLgB87Wtfq+p3CdHSpMYlx0WYOXOmt62envdHHL9js802C+oNHjzY20ceeaS3e/ToEdT7zW9+4+1evXoFZby28r7HxvWrF3jPYfeavBZynA5bj+OZvPbaa962a9CcOXO83bNnT2/bPS+3cb2kVq+E73//+8Hrxx57zNt8zW2sDG4bG5uS9/+8X02NSy6z9Xjd5bbhWB5AGIOF05BPnjw5qMe/xX4X30/VUkwbGwOV4xHxmLL7PJ4Pt9xyy6CMxxi3gY0lxMfnfW2qHRk73nhsjhs3ztu2vbkPcsyweoH33bF09kAYq4ZjlALhOsb93l6vm266qewxbDwohucHG1OJ5wDum3YeX7hwobcV00YIIYQQQgghhBCiAdFDGyGEEEIIIYQQQogc0uLyKCtP4RSUTz31VLTe2LFjvb377rsHZSyh4LTA1mWJXbHYdc2mc2NJFLtoWXdJTi/GbosvvfRSUI+P0a1bt6BswoQJ3t5tt93QqMRcTDn1MhC6InI7WZdrdkXktrZp/aw7cYmjjjoqeH322Wd7m13C7bk3evrvmKuoTWs4bdo0b7N7d0paw3OC7Rd77rmntx966CFvDxs2LKjHrpR2vHEq2pZOKZ4nrFuwlYGWeOKJJ4LXxx57rLdZ9mSv+fjx473N8+R1110X1GNp3Pbbb+9tm3KYpYuzZs0Kyp588klvz54929vWDV7yqM/GjmVuf26rPn36RD/XiHNgS2Fl18ydd97pbd4D2VSkPLZZUszjEgglBY888oi37R6IxyKnRAZCuTvLGjgdKlA/cy3v86zkgWGXeyvr6NChg7d57HCbAqGUgOdAuw/i9k6lLa43Bg4cGLzmPnfEEUd42+5LeF5jCRoQjgkei1bWEUsdzZIOIBxz/Bnbd1hayBIPey/BZT/84Q+DMl5PawmWEQHxPszzC5AOa8Hjj9vRzq+Vrl1cL5Xym8v4nKykh2WldjzzWOd9cy3B8yTb/CwACOc8277cVrzu2HXs73//u7eHDh3qbXvteK1KpfLmfSTLowYMGBDUS8mvqo08bYQQQgghhBBCCCFyiB7aCCGEEEIIIYQQQuQQPbQRQgghhBBCCCGEyCEtHtPmm9/8ZvB633339TZr+zidLBCm6uU02UCoKeU4Nlb/GEu7aLX7fAzWyFnN3csvv+zte++919s25gmnNL7hhhuCMqsDbxRsHI2YFpRTNQKhxpOvq9U8ptJqMjZVeImTTjopen6HHHJIUMa6yXqJ4ZBKcZj6jbF27NevX/Ca9ddTpkzxNqdnB8J4JtxWNqUn67u33XZbb//oRz8K6nH8hM6dO5c9VyAeOwmozdSpVpvLc6HVfE+dOtXbPK/ZWBQPP/ywt7nd7PXhNMH8Xe3atQvq8eu5c+d6+5VXXgnqcfwce+5HH320t1lzPH36dNQj1YgfM2PGDG9fcskl3ra672eeecbbBx98sLc53tfqnEeM3//+99622vEhQ4ZU9btqlV/84hfe5nHE8TCAcLxw3+HYArase/fu3rZtu95663nbxo3itYDXY45TBgD7778/6gG+NrFYeUA4F9s5kNN8M3ZdXHfddb29+eabe9vGYeF25bZqZO6///5o2fHHH+9tG1eTY9LwPYeNhcJ7Wx4Dtl5sj2HXNO5LvAY/+uijkV9RP3DqZAvfO9lYW5zqPpW2mceivQ+M7YFTcWt472OPx+fB587rLxDGTrLH4PiAtRrThuPH8Dxm76+5zN7D2fhQJewatM8++3ib1zFbj8czz5mp7+VnA7YeH8O2YbX3R/K0EUIIIYQQQgghhMghemgjhBBCCCGEEEIIkUNaXB5l4bTZDzzwQLQeyxpGjRoVlLE0IpZy2MKub1Y6wO6N7GZnpTRcj+Uel156aUXn0MikXMQ4hbtN6durVy9vx9xVgdAVjt0Pu3btGtSLpVHlfgkAzz//vLdPOOGE2KnXFCnJTCq9bDW48sorvb333nt7m6VmQOgiyTKbTTbZJKjHEoo99thjtc+P+2ee5VB2vuPXbKdceq2b9dVXX+3tM844w9ucDhWIS46sSzNfS5Yx2tSm3B/ZJdz2U+4TRx11VFDG/ZYlVsuXLw/qWalX3oitY02RKrI7NksQhw8fHtRjGRkzadKk4DWnSefradfj3XbbLXpOMcaOHRu8/u53v1v2PA499NCgXr3Lo2Lyt5kzZwb1OMUop4y1sieWBqRcuLke73Os5Jz3PRY+Bo/LF154IfqZWoavYWr95DLr5m9TgJfYbLPNgtcTJkzwNsujbMpbliKk1gBRINbvgXBNSqV3j83d9vrH7kHs5/k8YunELXbN5LmjliT8b731VvCa1zTe89vU7VtssYW37ViMXcPUNePPpO4x+fxs/+F9C5fZevy99pymTZsW/e68YkNXsGSXZUV2TeNxxem/gXh/tusTS0IrvefntrZ9h2VVS5YsidZjuZ59btChQwdUE3naCCGEEEIIIYQQQuQQPbQRQgghhBBCCCGEyCEtLo9KRehm20oSOPsMu5kBcXcy6wYXc9e18Of42Nb1lN3vU1j3N6ZRXVZT158zRtl+wFHX2RXRtjW7GbMLcpcuXYJ6nCGAv8tmYbjoooui53vqqad6+5ZbbonWa2lKYyvl/lqpCze72992221B2SOPPOLtkSNHNvk8AWDHHXf0Nmf84WMD8Uj/NjsHZzlKyaN4LLLrOBD2GXaDnDdvXlCvlMnDun22BXY+5Tbl62XdajlDwc9+9rOgjDP1sZsnSxMB4MQTT2zy+XKGwBEjRgRlnBmBJY3W/bxPnz7ethk+WJrF7WPnkZaWR5XaJZU5IDVOm+PCbuesCy64wNvcF1hmCIRZKFjuYrPNsKyK3ZWHDRsW1HvppZe8zdn+bBtwFkh77rvuuqu3WSI7efJk1DM2qwWvd9wnOMsXEGZUY0m3dQNPyTAYnid5nbV9gstslhb+Lbxne/rpp6PfW8tw+9gMQLx35LXLzl+xrFMs9wBCuTbLRFgaB4TSx9Q+VBSw2dZixDJEAfHMpXZ8pDINMXx8uxbGaGl5e2th914sS+M1yO7FeN609waxtbXS8VHptbWZgVm6w9ngbL/gOdrKJWNS5jxjrzf3Z95f2rFXaWblSuVMqYx+fJ15LNp5nEMC8B7StiGPU76XAiSPEkIIIYQQQgghhGgI9NBGCCGEEEIIIYQQIofooY0QQgghhBBCCCFEDmnxmDYpfVtKK2jj2DCsG2XNo9WjsWYxlVaNzyOV2o+/NwUfr5bS7VWblA6Y485ce+213h4wYEBQj2NzsEbUtoXV3pewekJOKRhL9Q6EsWpsOnDW6D/00EPePuigg8qeQ0uTSl2Y4qyzzvL2yy+/7G17LTjdL6fmBYDrrruu0tP0/PGPf/T2X//616CMry3Htnj//feDen/5y1+8zbGm9t1336Ae61Cthpa1sTx32FgcpRSrsfSsLUEs9a+dM7nduJ04JToA7LXXXt7+xz/+EZTxdea4NRw3yBK7dhbWoR9zzDFBGb/m2CV/+MMfgnqPP/64t62WmDXwMQ15a1Bqo0rHnh2z3Lc4taSN/cKpTt94442grHv37t7edtttvc2xg4Bw3uPxa6/ZPvvsU/bc7dzLY4zHok3HyZp1jskChDEKDjzwQG/bdPIlTby9frUEj+2U7v7BBx/0to2dximheV6zYzE2j6TqccwUG2eA28m2L8N96c033wzKbGyresCOe54f+XraeBs2tW2JL3/5y9Hv4pSyNjYKj6tG3ntWCs+ndizGYmfYuafSlNL8mvfDNrYK7z8qjfNRL9hU3na9L5GKIWXh687XOhVXiMvsPMdtx2Ob72mAcPylYhPxb7T7XBvjpxawbcZtw2W2b3M8PJs2O5aO3Y4xvs48nm0b8hiLjV8gjMHDc2u7du2CevzcgO2WQJ42QgghhBBCCCGEEDlED22EEEIIIYQQQgghckiLy6NSsKuadXdjNypbxq5N7FZu3RvZTYk/Y12q+PicMsy6gds0jDFiLsmNRiq9+aWXXuptdqm37sLs8h+TSgGVp0bkc+J+YGUn3Jes2yO7RD788MPetvKb448/vqJzai5NlWRYttlmG2/fcccd3i7JgUqwK75N93v++ed726YWjsFjkWUcQCjX4OvOKYEBYODAgd7u16+ftzmdMQDssMMOZY9n4TnAumZuvPHGACrvY02h1AdtG8ba9Prrrw9es7SJ23Po0KFBPZYY2bLnnnvO2yxNSc13fH6pNNeVzoUskbQuwbxOWPdhHnM8d1h5bZcuXaLfXU3s2hJLc23dwDkdPUtQrOyTXXntdWdJxbPPPuttdjsGgE022cTbpb4NrDp/devWDeVgl2EgHMOc4t2OF16rbcpWTi/Prscs/wNWSsdSLs3Vhq9zzLakJNKpcXDZZZd5++c//7m3t9pqq6Ae/34eH6m0s6nzjckdbX/m9dOu77GxzuslAEyYMCF6HrVESkLBe0/eR9p9RkzWvf322wevuR24rVKy85RkRBRgKaqVhfO9AM9lVs7EbZMa2zHJnJ0L+TxSYSLqEb7mQDjGuIzbDQjbp9K1wbYVfxe3sZ2/GK5nxzbPCTwW7X0lzxV2Hqk0LXmesP05FqbE7oF47kq1TWp94td8fLsX4XWSz9deb/4uTuVtZdu8x5I8SgghhBBCCCGEEKIB0UMbIYQQQgghhBBCiBzSpvKoFO+88463bTaXmPuRddu2nyth3dhiUqxUlinOmGHdyFNuyPVM6npZOBsGyxpYKgWErqIsg7EZKThzDktkbMafWJ+wsCumdWHmTDzNyZ5UDbIs866U1h2R5QUpd91vfetb3uYsTlY+c/HFF3t7p512Cso4Ewgfz7bjiy++6O0ZM2Z4247l/v37e5tdxK17I7tScnavMWPGBPX4PFi6AYTSO+67PLaBlZKhlpA6psZIOexcwzIxdgu2Ere+fft62/6+7bbbrmxZKnNFSvrIpK4Z950bb7zR2/vvv39Qb/r06d622eDY7Zj7vT2/lpRHLVu2DLfffjuAUC4IAN/4xje8zdkHrCs+u9fyb7Tu8YsXLy57vNJ5lOAsfLYv8Nx2xhlneNuuWzx38nizLsQ89zKLFi0KXrNLu52H+bvGjRvnbZb/tRXch6sxBwwfPtzb5557blDGEmCWndl5gtuQ+4iVBnA93tukJFupzDYp+Q3XZTd/K1u3bvG1SioTDe8peXykJI1MLKsUEPaFlBSkkaX5lcpyed23MhiWrHJ72H1ubL+fklqkpHX8OiXNqZcwDCnZOu8Pef2wWWa5fax0hfcxqSzC/JrXp1gGKyCc2+xY5N/FkmS7l+XfZfsS9xk+j0rvY9oCK13j/sxjIiXftPs3/u08/6UyOqfGNkvB+Xh2LPK+jNdS23f4+Pbep9rI00YIIYQQQgghhBAih+ihjRBCCCGEEEIIIUQO0UMbIYQQQgghhBBCiBzSpjFtUjrMF154wdtWv8e6M9aW2RgMrEHjMquXi6X2S+nDWa9v40zwOVUa+6GWiGlpU/E5HnzwweA1x85gPapN+cfaQ073a1NFc5+YPXu2t62umL+Lz9emlWd69+4dvP6///u/aN3W4uOPP8Zbb70FIIwNAoTtw33RxhbgWAisHbZpvbme1ZCedtpp3uZYBVYHzJ/j9LVWz8zxTF555RVvd+3aFTFYe77bbrsFZRMnTvT23nvvHZRxH+SxvuWWWwb1Sv0kD7pxmwI5Fk/E6rpZc8sxgIAwxXapT30WfL3mz58flHF7cGwyG3OMv/f+++/3tk0dv+GGG3rb6sF5beA+ZuM8pcb36rL++uvjgAMOKPs93F6TJ0+u6HgcQ8vOhzNnzvS2/S4eS/w5ewyeE7kdud3s53iutOsxj3WO1WPbiueR1Fjitdr247FjxwJYtX3bgqVLlwavn3jiCW+PHz/e2w899FBQj/vBFltsEZRxHC++rrYNua9XGkMvltbbwu1m52fez9hj8Dnxd6XWglom1T4cl4rnR3s9Oa4aY+Po8Zjja5tKyV4v17klicXbAMI9EV//VFpqPobt9zxeuJ1sG3IfaWrMu1oktocBwusZi0MCpOPOcN3U9ax0Ho3FPbXxUHj88d7HxnLhmH02Vg8fk+85U/vhtsa2Bf8G/q2233fq1Mnbdq8Uu1exxNrXzoV8L8n7SxsTk+Mkclwiuy/hvmNj+lSb+p8RhBBCCCGEEEIIIWoQPbQRQgghhBBCCCGEyCFtKo9KuapxSueUnIldr6w8KpbKOyVZYtcr68bGx+PUnJwyF8iHjKIlac7v47TRQJiWm1PS2nRp3B6c/vW5554L6rGbOferp556KqjH/YDlQin3ypQ7HmPdK1uyH6yxxhreZZDlRkB4LV5//XVv2zTD7OLHqYltimCWZJx55plB2aGHHuptHgfWVZTH5htvvOFtK9WZNGmSt1nKZt3K+fjcdnxse4xRo0YFZSyxY1dNlu0AwMYbbwyg+hKbDz74wPfjBx54ICjr3Lmzt/k32DmJ3W65n1opHKdUnjp1alDG/Zbd9R999NGgXkxeYWVPMdmhlctwv+XP2Dngtdde87Ydp7E0mDbd9P/7f/8PLYVzzn/3scceG5TZ16tLLPUlEI4RvhZ2XorNZ3Zd5GOw3ZbrW8mt2c5l1eTpp58OXl9yySXe5r5p+xinlefxZvs9SzjtteR5jctS7t38GVuPrxP3HeuazvMFl9nz47nIykn4NfdNvhYAsPPOO6PeWLx4cfA6JjGz6+Jmm21W0fFZHsDHs/M8j23+TKNRaTpsvl52XuSxxPOf7c98fDsmmNj6ab+Xy1hybqmX+4wVK1Z4214LnotYEmv3jTwm7D6Nj5GSQHHbpa5t7P4xNZfz/rdv375BPV5T7PzN52T3WXnFyqf5N/A14XTatszKn2Nzmb0mfJ15nLK0DgivM69pfK8DAF/+8pe9vcMOO3jb7o379evnbdvH+B7M3qs1B3naCCGEEEIIIYQQQuQQPbQRQgghhBBCCCGEyCGtLo9iVzV2obWZmtjd1Mqe2A0t5e4Wc0+z7nN8jFRUd3b7YnmUpREivpeItScQZv/hbBoA0LFjx7L12FUNAHr16uVtdiVm93AAGDdunLfZlW7IkCFBvRdffNHbKbdy7mMcQTxFa7qrrrHGGt41mjP0AKFLLbsFbrTRRkE9jtrP7WHlKRzRfs6cOUEZS6JY2sQR2YEwGwbLCKwUid1jOeORlUfxa+6D/DuA0DXTtuOCBQu8zfOPnVNKkqRqj+u11lrLu8raNuTXnKWGI9gDoYyKr511B+WI9vZashsp//ZLL700qMeSRnZftXM3w8ez15X7C7eNbSceVykZI7uy2ut58sknRz+3uqy55ppehmKvO7/mvmhdrHlNStVj7HzD7cpjxx7DrmslbPvE1lb7Ph+PbdvPuJ+kflcqI0dJqhj7Dc3lk08+8XLR73znO0EZrw2cFYhtIGxD7sN2beH52UqnmNh1BSrPSslzFn+X7Tu8ZvIYs5nhUtk6eU5ISXh23333is4973Ab2IxCscyEdv6y2cNisJSA5SQ2yxS3a73IZ1oSltnYPSXPoak1KHY/YqVSPD/YuZHh/pJaW+uF1D48Nqd85StfCepxllArz6n0njO2p7TtyJ/j41lpF58H/0YbfuCee+7xtpXexTJQ5Rk7F/I8ydfY3pvF1iogLju09/I8FlNjh4/P86ltG4bXeyvt4nay8261s0k1ztMFIYQQQgghhBBCiBpCD22EEEIIIYQQQgghcoge2gghhBBCCCGEEELkkFaPaRPTydtYAKwZs6k1OY0l61BTqcYYq3WL6VCtNo0/xynJLazNa8000NUkpafn35eK83Heeed5m/XBQHgduMzGouA031xvyy23DOpxPAtOZT179uygHqfb41RsNkYAaxRtfJY8wHE0bBvw+EjFIGA9PI83q6vllMu2L/DYZD1opXphG8OIU+Jx7AeO3QKEbce/y2rFObaEjenDGuQePXqUPTawMt5DpXEkKmXNNdf0v/+YY46p6DN2TuPfwJpn24Z8zVPpD1mvb+cq1v7y8ThGERCOU+4HNo0jH4/r2Xmcf4ttA+7rnA69W7duQT3b9i2FTUXdkqmpGxmrZV9dFi9ejOuuuw7AqnF0eA6JpUsHwnmY293GIuA+bMt4zPFvtHM8x4nh46VS5vL4sL+R45hxrK9OnToF9Xgetrp+Xk/4nHicA+k4PvVCLP6E3Q9WOi/xfDZ16lRv231Vah5tJGJxTOz15zXJzil8/VKpvGP3NHaM8ZhIxfdKxV2p9Bxq5T4DSM/l/Lu4nt3L8r7FjqlKY9rwvMT17J7StmsJOx/yMXh+tbFceA9s4yVxrDG7b8srNi4R/waer1Kxh1LwGmfjH/J3c2wduw/j+0z+3t69e0frcbxMu7/mPtG9e/egrNrrnTxthBBCCCGEEEIIIXKIHtoIIYQQQgghhBBC5JDcyKNsmmF2BUulNmW3JFuPXc1iqYTt59ilyqZTZPe8mKQACN2+mpuqszWwbcG/g39fpamOr7zyyuA1p9feY489grLRo0d7m6+JdXeLpcu0qUithK7ETTfdFD0nTkNu3ef4u2wa6TzgnPNtZK8Zp6PnduQU3wDw7rvveptdGFMpfS18nVjOZNPc8ZhlSaM9Nh8vld6Z24v7p+0XPK+wPAAIXWx5DuAU5/b4bY2dP9j1ku1SamQhxGfjnPPru5W28LzBa7uVIvE8wRKjlHTB7kV4TuLjpSTKfB52ruJ5l129rav90KFDvf3zn//c2yNGjAjq8W9JydHZfbzaKU/ziG0flt1wf7LXrFLpNc/nLN+10jN+3bVr14qO3UhYmRP3Zzt2KpUpcVlqrxQrs/Ig7i/2HqQeSUnUeH7kuSwlj+K5FwjHGN/f8X4VCMcYl1m5D7fJ0qVLvT1nzpygHsueeD6096l8vv369QvK+PfbVNp5xa53PD543873HEDY7+0+l8cOz6F2XMZkh3bscT1uJyuFW7x4sbf5WcMOO+wQ1OM+zNJloPpjOD93I0IIIYQQQgghhBDCo4c2QgghhBBCCCGEEDmk1eVRMWzGFnbftdHAWeaRkimxC1RKHsXHYPdS68rKn+PjscwEADp06ODtmBwsD1g3PesGWMK6lLIb4O9+9ztvX3311UG9nXfe2dvWZXGXXXbx9rhx47zN0hkgLmFJSVaGDx/u7YMPPjgoe/jhh8t+xh6P283Kj2L12ipi/+GHHx68ZlfAN954w9u2DVhSNmPGDG/baOfc1637Kl8bHm+9evUK6rGLaizLCBBmJ+HPVCpRsn2Vf6Mdz+wKmZLoCSHqm86dO+Oiiy4CELpEA8DIkSO9zXOonSd43mXXbztnpubTWNZGWy8mnWJ3fVvv7LPP9vZZZ52FSrjtttuC1zw/W5dzXqt5LxbLtlJPpNz52XXeyrBjey4LZ6bhz9h+wde62hnW6oFUdjUruYlle7LwMVLZZ7keH89+L7dvLAuZpZazR3GftZIlvrfiucxm7+Hfa9s4lgXMzks87/P96E477RTUi8mo7N6T72X43G1GPn7N2VOBcP9eK/OonQv5uvCem++TAWDMmDEVHZ/Hi73mPHZ4TFg5MN/L277E8DrO9702e/Gzzz5b9vyAVSWsq4s8bYQQQgghhBBCCCFyiB7aCCGEEEIIIYQQQuQQPbQRQgghhBBCCCGEyCG5Eb1aLSNr36w2l3WOrPW1+jbWOcb0pECoW2P9mU3VFdOu2lghVqtXK9x3333e/vrXv+5te72sZrSE1fhPmTLF24MGDQrKJk6c6O0+ffp4e/LkyUE9/m7WptprPmzYMG/bODaM7SMxuO906dIlWo/7RF7SubN+lrWXVodZj6Ri5AghxGdx7bXXBq85Pss111zj7VtvvTWoxym1Oe6ejRHG+wob04TThfL32lSk/F38mQsvvDCod8EFF2B14HUaCHX9dq7l2C0dO3b09sKFC4N6tRKb4bPgfZBNX8vxFDhVb2ovkaJnz57e5utn4zMyjRzTJhZLstIYMfYYqVg1sdTgdq8Za49UXA4eU/UKxxRJxRfha/vSSy8FZXzP9fbbbwdlfD35+LY9uB34eHZs8zH4M3wvCoT3Mhyf8fHHHw/q8dxu74M5PoqdR2sRmw6b4bkslcqb282uQbE4VDaeJc/JPMbsPSzHIOP1mNOEA+n7ShvjZnWRp40QQgghhBBCCCFEDtFDGyGEEEIIIYQQQogc0ur+kzG3xZkzZwavrTsTw+5pvXv39rZ1IWZYUsWuakDcfY5dqIDQzYllMTZNNZPnlN/z588PXp9zzjneZhc06x4Yw7qIcXu88MILQRmn0eN00/a7OGUzu7YedthhQb1DDz20onOMpdi0bnbs8mpd4Zg8t68QQoimYVNZ816E10i2LZwmfNy4cUEZu83Pnj07KGN5NruSWxfrM844w9vnn39+9Dxi2N/I6x1z+eWXB6/XXnttb9u1lNduXjOtNLpeYHd5u39gCRPvg1J7iRScZpjlOVaqw9+bF7l2nuD7AKDyFN1s2z1f7F7FSjx4T83fm5JWcB+rV3iPv9lmmwVlPB/yfZZNm81SRTtX8n0ct5Vtx5jcMTW2uYylNEB4v8LSHysP5e+aNm1aUMZ9ppbSuDM8//Xo0cPbNg33a6+95u1+/foFZTGpoh1jXMZtbWWkLDXj/mLXQT5GKmRL7DP2nKqBPG2EEEIIIYQQQgghcoge2gghhBBCCCGEEELkED20EUIIIYQQQgghhMghuckJaHWDrEFLxZZh/ZjVCrKmkFOp9erVK6gXS0FptWh8jqw1TWkNrXY8TwwfPjx4zdeINaM2TSJfF9aBpnTyVj/6yiuveLtbt27eHjx4cFCP4wHMmjXL2w888ABisPbQ6ltt+tUSqTSkm2yySbRMCCFE/RCL79IU9tprr7J2Xqj0N55yyiktfCa1C+8BUzEYee/DMYFS9eyekvfDqRgMHFcjlQ683qk05XfqmsfGSGpPn4pfwXvMVN+JxdKpV2Lxn4Cwry9ZssTbtq04/peNi8njIBWDkuPn2HtEJjaebVt17NjR29yX7Pml4ufw70/FUckTNsbr3LlzvT1gwABv23hufH+37bbbBmU8rvg62GvO169Lly7eXrp0abQet6GNs8NlHHvJ9j8+p8WLF0e/qxrI00YIIYQQQgghhBAih+ihjRBCCCGEEEIIIUQOyY2/lU0fyS5FVrrC6Q/Z7cxKYWKp2TbaaKOg3kcffeRtls9YV7qYDCrlhloNV+uW4uSTTw5e33PPPd6eOnWqtzkNOhBeB3ZPS6XNZvdF+7m33nrL2+yCBoQui0899dSqP6IMTUnHFvsMp5JLpTyPue0JIYQQov7ZcMMNo2XsYm/3QUxqL9W+fXtv8z7D7i95b9bI8qgYVh5VaUptvj9JSaB432ivP7dvpfcMLPGo9H6k1uB7Liut6dmzp7c5XbuVoPA9ipUgcl2+tjZUAu/zWaZlw3MwfL62Hn8Xt9WcOXOCenxPYu9PeF5JSbbyRN++fYPXfN7t2rXztpUsHXLIId7me3Ig7Ps8/9kxwc8AeMxy3wGA9dZbz9s8J9h5l+drlufZZxKHH364t20fTklnm0N+nygIIYQQQgghhBBCNDB6aCOEEEIIIYQQQgiRQ3Kj55g+fXrwmmUx1r1o+fLlZW3rssTuVxwV+s033wzqLVy40Nvjx4/39s477xzUYxc8dsuy0q5awbrqPvnkk95+++23vX3LLbcE9f7xj394m7M7pTIwVQpnowKAhx9+2NtDhw5d7eNvvvnmZd/n/gYAvXv39vY222wTPV61I4MLIYQQIt+w7MLKujt06OBt3tOkZDEpeRTvgVm6YSUyLPexsvZGIiYdsteE9/EsfwCAefPmeZtlHTZjDR+D5VFWusayKu4v9ngsJ5k8ebK37R41JcmrJXh/baVNEydO9PYvfvELb9tQBHyvx2MPCGVLb7zxhrdt9lyWYnHb2XtTvu7c3vvtt19Qj9uV286eH0t3xowZE5RtsMEG3t51111RC3AGu3KvS/C9o8WGOmGsxJHhdmOZkp1P+Rj2npPh+YLHr5W4bbbZZt5m6VVLIE8bIYQQQgghhBBCiByihzZCCCGEEEIIIYQQOUQPbYQQQgghhBBCCCFySKvHtImlwB48eHDwmvWlnOIbCPVuHTt29LbVrbEmle1BgwYF9VgjPHv2bG9bXSzrLTn2TadOnRAjzym/U3Tr1s3bF154YVBmX5ew2s8ZM2Z4m2MPAWHadY4fE4s5Uy3OOeccb2+//fbetn2Mz4/TbVqU5lsIIYRoLPr16+ftgw8+OCjjWBe8l9hzzz2jx0vtFXmPyfETbJwT3g+nYvHVO7F92f777x+8HjFihLdnzZoVlPE9CMe9sHFxOHYGx3W07cnxhjhGjr1/4NTTvDdOxbCp5fTfnCL6vPPOC8qee+45b3/ta1/zdjXiiF500UWrfYxqwDFtzjzzzKBsyJAh3q6Hew2eF23cGo73ZePM2PirJWy8Wx5jfDx77TgeGc+ZNl4Ox+Phc4jF6QFWjVFV7WcAtflEQQghhBBCCCGEEKLO0UMbIYQQQgghhBBCiBziOF3dZ1Z2bjGA2Z9ZUVSTTbMs6/jZ1SpDbdhmqB1rH7VhfaB2rH3UhvWB2rH2URvWB2rH2kdtWB9E27FJD22EEEIIIYQQQgghROsgeZQQQgghhBBCCCFEDtFDGyGEEEIIIYQQQogc0uYPbZxz7Z1z44v/Fjjn3qHX0bxuzrmezrnJkbJLnHP7RMpOdc51Me8d65z7iXNuqHNul9X7RY2Jc+5Q51zmnNuqwvqznHMdyrz/Qbn6ieM0qX7iOKv0C1Ge4liZ4pybWBynO1bhmE875wavbh1ROWrH2qQl2o2OPdQ591C1jtfoaF2sb5q7fxWtj+41Gpty62Zivv2ac+78yHHUdm2Ic66Tc+4u59xbzrmxzrmHnXNbNPEYGzjnvttS59iStHni9yzLlgIYAADOuZ8C+CDLsl+v5jEvLve+c25NAKcCmAxgHhUdAOBaAAcD+ADA6NX5/gblOADPFf//3zY+l+ZwKlbtF8LgnNsZwEEAtsuy7OPigqfNaY2hdqxN8txuzrnPZVn2n7Y+j5yhdbGO+az9a2uPCefcmlmWfdpa31dL6F6jcWnqupll2XAAw8sc53MAhkJt1yY45xyAYQD+kmXZscX3tgWwCYDpTTjUBgC+C+C6ap9jS9PmnjaV4Jzbxjn3cvHp6ETn3ObFojWdczcWn54+5pxbq1j/FufckUV7lnPuCufcOBQ2ToMB3FE81lrFTjAAwDIA3wbww2LZbsUn7COL3/mkc64HHf8G59wY59x059xBrXxJcoVzbl0AQwD8PwDH0vtDi39Vv88597pz7o7i9ebPruWce8Q5960yxz3HOfdK8fr/LPH9Vxf7wJPOuY7F9wY4514sfnaYc27D2PvFvhL0i6pcmPqkM4AlWZZ9DABZli3Jsmyec+7iYltNds79qdTOxfa/ojh+pzvndiu+v1bxaflU59wwAP6aO+euL46tKal2F6uF2rE2ibXbLOfcz5xz45xzk1zRs8M5t45z7uZiu73qnDuk+H5P59yoYv1xrsxfDp1z2xc/08c5N8g594wr/GVrhHOuc7HO0865a5xzYwCc2XqXIf9oXWxM3Mr94UsAfpW45t7j0DnXwTk3q2iX3e86506k9//oCg8G4Jz7wDl3lXNuAoCd2+RH1wmxaw/da9Q6ZdfNYtn3y6ybpzrnfl+0eTzfA9N2bfBbGpk9AXySZdkNpTeyLJsA4Dnn3JWusG+d5Jw7BiiswcXxVGrfQ4ofuxxAn2IbXtn6P6P51MRDGxQGyW+zLBuAwkT4dvH9zQH8IcuybQCsAHBE5PNLsyzbLsuy2wGMAXBClmUDsiz7J4CBACZkWTYTwA0Ari6WjQLwOxSe6PUHcAcKT8hL9ASwA4CvArjBOfelqv3a2uMQAI9mWTYdwFLn3CAqGwjgLABfBtAbwK5Uti6ABwH8NcuyG/mAzrn9UGjfHVBY6AY553Yv893rABhT7APPYOVfM28FcF6x7Sal3s+y7D6s2i9EeR4D0L24gbjOObdH8f3fZ1m2fZZlfVG4cefNxeeyLNsBhX5QaofvAPgoy7Kti+9xn/lJlmWDAfQHsIdzrn8L/p5GRe1Ym8TaDShsSrcDcD2A/ym+9xMAI4vttieAK51z6wBYBGDfYv1jEK5tcIWHODegMLfPQWEtPDLLskEAbgbwC6r+hSzLBmdZdlW1f2yNo3WxcekGYJcsy85G/JrHWGW/65zbGoVxumvx/U8BnFCsvw6Al7Is2zbLsueq/ksaC91r1CdNXTctpfF8OFZtO9F69AUwtsz7h6OwHm4LYB8U9jmdAfwLwGHF9t0TwFXFh6fnA3ir2IbntMqZV4laeWjzAoALnHPnoZC/vLR5mJll2fiiPRaFya0cdyeOvT+ARyJlOwO4s2jfhsJfzUrck2XZf7MsewPADAAVadbrlOMA3FW07yq+LvFylmVvZ1n2XwDjEbbR3wH8OcuyW8scc7/iv1cBjEPh+m5ept5/sbJ9bwcwxDnXDsAGWZY9U3z/LwB2j71f6Y8UQJZlH6BwY34agMUA7nbOnQpgT+fcS865SQD2ArANfeyB4v88RndHob2QZdlEABOp/tHFv1a9WjzOl1vkxzQwasfaJNFuQPn22Q/A+c658QCeBvAlAD0AfB7AjcV2vhdh22wN4E8ADs6ybA6ALVHYLD1ePM6FKGxiS6TW10ZG62Ljcm+WZZ8289qW2+/ujcK4f6U4BvdG4WEfUHiAc3+1f0CDonuNOqQZ66blXskOc80QFP7I8WmWZQtR+EPF9gAcgF865yYCeAJAVxSkVDVLm8e0KYdz7jCs/GvEN7Msu7PomvZVAA87505HYfL6mD72Kcg13/Bh4uv2Q/ypeYrsM143BM65jVC4uevnnMsArAkgc86Vnl7aNuI+9zyA/Z1zd2ZZZq+fA3BZlmV/bOIpNWQ7tCbFxetpAE8Xb/pOR8GbYnCWZXNdQS/Ofw0q9QHb/qvgnOuFwl87ts+ybLlz7hZzLFEl1I61SZl2O6VYVK59HIAjsiybxscotu1CFP4ytQYKf5EqMR+FthqIQjwGB2BKlmUx6UVqfW1ItC42PJWMif9g5R9O/dwY2e86FDwxflzmOP/SDWXz0L1G49DEddOiNS4fTAFwZBPqnwCgI4BBWZZ94goS1Jreh+bS0ybLsmFFt6UBWZaNcc71BjAjy7JrUfgr1Oq42b8PYD0AKP4V5HNZIUBZUFZkNFZq0U8AwK5wRznn1nDO9UHhLx7BpriBOBLAbVmWbZplWc8sy7oDmAmgEq3nxQCWA/hDmbIRAL7hCnEB4Jzr6pzbuEy9NbByEB8P4Lksy94FsJz0picBeCb2ftG2bS/K4Jzb0q3UeQMFl8RS319SbK9KJtVnUWgvOOf6YuWYXh+FBfJd59wmKATuE1VG7VibRNptduIjI1DQ7JdiEw0svt8OwPyip8dJKDxUKLEChZuWy5xzQ1HoFx1dIZgjnHOfd86xB5ZYFa2LAp9xbWdhpZzUz7WR/e6TAI4stbVzbiPn3KYt/wvqG91rNAbNWDdTaE5sO0YC+KJz7rTSG0XZ/QoAxzjn1nSF+G27A3gZhX3OouIDmz0BlObMmm3DXHralOFoACc55z4BsADAL1G4KWgOt6CgC/0ngKtQcJkq8SCA+1whWNH3i//+XPzr2GIAX6e6c1DoFOsD+HaWZfyXykbiOABXmPfuL75fidv8mQBuds79Ksuyc0tvZln2WFHH/ULxfuMDACeiEIuB+RDADs65C4tlxxTfPwWFdl4bhb+UfP0z3r8FK/vFztLvR1kXwO+ccxug8JfCN1FwOV2BQqaEBQBeqeA416MwtqYCmIqiTjXLsgnOuVcBvA5gLgp/dRbVR+1Ym8TaLRag8ucArgEw0Tm3BgoPDg5CIWvC/c65kwE8CvOXxCzLFrpC0MtHAHwDhZvKa0s3H8VjTqnmD6sztC6KErFr+2sA9xRvQP5B9VfZ72ZZtqzYlo8Vx/EnAL6H5t94ivLoXqM+aeq6mSJoO8W1aT2yLMuK3nHXFCWM/0Lh4fdZKLTxBBQ80c7NsmyBc+4OAA8WPavGoLAfRZZlS51zzzvnJgN4pJbi2rhVvW8bB+fcTQBuyrLsxSZ+7hYAD2WFQH1CCCGEEEIIEaB7DSFENagVT5sWIcuyb7b1OQghhBBCCCHqD91rCCGqQUN72gghhBBCCCGEEELklVwGIhZCCCGEEEIIIYRodPTQRgghhBBCCCGEECKH6KGNEEIIIYQQQgghRA7RQxshhBBCCCGEEEKIHKKHNkIIIYQQQgghhBA5RA9thBBCCCGEEEIIIXLI/wfgSIDT2WWWMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "for i in range(20):\n",
    "    plt.subplot(5,10,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、构建CNN网络"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "卷积神经网络（CNN）的输入是张量 (Tensor) 形式的 `(image_height, image_width, color_channels)`，包含了图像高度、宽度及颜色信息。不需要输入`batch size`。color_channels 为 (R,G,B) 分别对应 RGB 的三个颜色通道（color channel）。在此示例中，我们的 CNN 输入，fashion_mnist 数据集中的图片，形状是 `(28, 28, 1)`即灰度图像。我们需要在声明第一层时将形状赋值给参数`input_shape`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 3, 3, 64)          36928     \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 576)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                36928     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 93,322\n",
      "Trainable params: 93,322\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = models.Sequential([\n",
    "    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)), #卷积层1，卷积核3*3\n",
    "    layers.MaxPooling2D((2, 2)),                   #池化层1，2*2采样\n",
    "    layers.Conv2D(64, (3, 3), activation='relu'),  #卷积层2，卷积核3*3\n",
    "    layers.MaxPooling2D((2, 2)),                   #池化层2，2*2采样\n",
    "    layers.Conv2D(64, (3, 3), activation='relu'),  #卷积层3，卷积核3*3\n",
    "    \n",
    "    layers.Flatten(),                      #Flatten层，连接卷积层与全连接层\n",
    "    layers.Dense(64, activation='relu'),   #全连接层，特征进一步提取\n",
    "    layers.Dense(10)                       #输出层，输出预期结果\n",
    "])\n",
    "\n",
    "model.summary()  # 打印网络结构"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、编译"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在准备对模型进行训练之前，还需要再对其进行一些设置。以下内容是在模型的编译步骤中添加的：\n",
    "\n",
    "- 损失函数（loss）：用于测量模型在训练期间的准确率。您会希望最小化此函数，以便将模型“引导”到正确的方向上。\n",
    "- 优化器（optimizer）：决定模型如何根据其看到的数据和自身的损失函数进行更新。\n",
    "- 指标（metrics）：用于监控训练和测试步骤。以下示例使用了准确率，即被正确分类的图像的比率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 四、训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 9s 4ms/step - loss: 0.7005 - accuracy: 0.7426 - val_loss: 0.3692 - val_accuracy: 0.8697\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3303 - accuracy: 0.8789 - val_loss: 0.3106 - val_accuracy: 0.8855\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2770 - accuracy: 0.8988 - val_loss: 0.3004 - val_accuracy: 0.8902\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2398 - accuracy: 0.9097 - val_loss: 0.2898 - val_accuracy: 0.8968\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2191 - accuracy: 0.9195 - val_loss: 0.2657 - val_accuracy: 0.9057\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1952 - accuracy: 0.9292 - val_loss: 0.2731 - val_accuracy: 0.9036\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1791 - accuracy: 0.9322 - val_loss: 0.2747 - val_accuracy: 0.9056\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1576 - accuracy: 0.9416 - val_loss: 0.2750 - val_accuracy: 0.9049\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1421 - accuracy: 0.9461 - val_loss: 0.2876 - val_accuracy: 0.9032\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1330 - accuracy: 0.9509 - val_loss: 0.2769 - val_accuracy: 0.9144\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_images, train_labels, epochs=10, \n",
    "                    validation_data=(test_images, test_labels))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 五、预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "预测结果是一个包含 10 个数字的数组。它们代表模型对 10 种不同服装中每种服装的“置信度”。我们可以看到哪个标签的置信度值最大"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1eab737ed60>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_images[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pullover\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "pre = model.predict(test_images)\n",
    "print(class_names[np.argmax(pre[1])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 六、模型评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 0s - loss: 0.2769 - accuracy: 0.9144\n"
     ]
    }
   ],
   "source": [
    "plt.plot(history.history['accuracy'], label='accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label = 'val_accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim([0.5, 1])\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n",
    "\n",
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试准确率为： 0.9143999814987183\n"
     ]
    }
   ],
   "source": [
    "print(\"测试准确率为：\",test_acc)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8rc1"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "165px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
